Course Name Code Semester T+U Hours Credit ECTS
Big Data SWE 307 5 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language English
Course Level Bachelor's Degree
Course Type Compulsory
Course Coordinator Dr.Öğr.Üyesi MUSTAFA AKPINAR
Course Lecturers
Course Assistants
Course Category Field Proper Education
Course Objective

Big data concept, visualization and analysis methods are introduced. The use of frequently used tools in Big data  is explained. In addition, the necessary infrastructures for developing big data applications and introduction to Python is introduced.

Course Content

Fundamental concepts in data science, big data analysis, visualization, tools and applications.

# Course Learning Outcomes Teaching Methods Assessment Methods
1 Big data analysis and learning the basic concepts Lecture, Question-Answer, Drilland Practice, Testing, Homework, Project / Design,
2 Understanding of model development with programming Lecture, Question-Answer, Drilland Practice, Testing, Homework, Project / Design,
3 Learning big data infrastructure systems Lecture, Question-Answer, Testing, Project / Design,
4 Having information about the graphical representation of data Lecture, Question-Answer, Drilland Practice, Group Study, Self Study, Project Based Learning, Project / Design, Performance Task,
5 To comprehend basic methods in search engines and suggestion systems Drilland Practice, Group Study, Self Study, Project Based Learning, Project / Design, Performance Task,
Week Course Topics Preliminary Preparation
1 Introduction to big data, basic information
2 Introduction to Python
3 R language
4 Data analysis and visualization
5 Hadoop Systems
6 Striim, Cloudera
7 Machine learning: linear regression, classification, clustering
8 Apache Spark, Hive, Cassandra
9 Spark with No Sql, Kafka system, RabbitMQ
10 Spark ML library applications
11 PageRank, search systems
12 Analysis with Tensor Flow, VoltDB, Data Flow
13 Big data applications
14 Evaluation of projects
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions.
2 An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations.
3 An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently.
4 An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods.
5 An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research.
6 Understanding, awareness of administration, control, development and security/reliability issues about information technologies.
7 An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility.
8 An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language.
9 An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology.
10 Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society.
11 An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications.
12 An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 40
1. Kısa Sınav 10
2. Kısa Sınav 10
1. Performans Görevi (Uygulama) 40
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 14 3 42
Hours for off-the-classroom study (Pre-study, practice) 14 1 14
Mid-terms 1 15 15
Quiz 2 4 8
Performance Task (Application) 1 25 25
Final examination 1 20 20
Total Workload 124
Total Workload / 25 (Hours) 4.96
dersAKTSKredisi 5